A Personalized Pulmonary Circulation Model to Non-Invasively Calculate Fractional Flow Reserve for Artery Stenosis Detection

Hemodynamic analysis of unilateral and bilateral renal artery stenosis based on fluid-structure interaction

Renal artery stenosis (RAS) hypertension is a common type of secondary hypertension. This paper aimed to explore how unilateral renal artery stenosis (Uni-RAS) and bilateral renal artery stenosis (Bi-RAS) caused renovascular hypertension with the fluid-structure interaction (FSI) method. Based on a real RAS model, 20 ideal models with different stenosis degrees were established by modifying the stenosis segment. The hemodynamic parameters at different degrees of stenosis, mass flow rate (MFR), pressure drop (PD), fractional flow reserve (FFR), oscillatory shear index (OSI), and relative residence time (RRT), were numerically calculated by the computational fluid dynamics (CFD) method. The numerical results showed that RAS caused the decrease of MFR, and the increase of PD and the proportion of high OSI and high RRT. In the case of RAS, it could not be regarded as a reference indicator for causing renovascular hypertension that the value of FFR was greater than 0.9. In addition, the results of the statistical analysis indicated that Uni-RAS and Bi-RAS were statistically different for MFR, PD and the proportion of high RRT.

Quantification of functional hemodynamics in aortic valve disease using cardiac computed tomography angiography

BACKGROUND AND OBJECTIVE

Cardiac computed tomography angiography (CTA) is the preferred modality for preoperative planning in aortic valve stenosis. However, it cannot provide essential functional hemodynamic data, specifically the mean transvalvular pressure gradient (MPG). This study aims to introduce a computational fluid dynamics (CFD) approach for MPG quantification using cardiac CTA, enhancing its diagnostic value.

METHODS

Twenty patients underwent echocardiography, cardiac CTA, and invasive catheterization for pressure measurements. Cardiac CTA employed retrospective electrocardiographic gating to capture multi-phase data throughout the cardiac cycle. We segmented the region of interest based on mid-systolic phase cardiac CTA images. Then, we computed the average flow velocity into the aorta as the inlet boundary condition, using variations in end-diastolic and end-systolic left ventricular volume. Finally, we conducted CFD simulations using a steady-state model to obtain pressure distribution within the computational domain, allowing for the derivation of MPG.

RESULTS

The mean value of MPG, measured via invasive catheterization (MPGInv), echocardiography (MPGEcho), and cardiac CTA (MPGCT), were 51.3 ± 28.4 mmHg, 44.8 ± 19.5 mmHg, and 55.8 ± 25.6 mmHg, respectively. In comparison to MPGInv, MPGCT exhibited a higher correlation of 0.91, surpassing that of MPGEcho, which was 0.82. Moreover, the limits of agreement for MPGCT ranged from -27.7 to 18.7, outperforming MPGEcho, which ranged from -40.1 to 18.0.

CONCLUSIONS

The proposed method based on cardiac CTA enables the evaluation of MPG for aortic valve stenosis patients. In future clinical practice, a single cardiac CTA examination can comprehensively assess both the anatomical and functional hemodynamic aspects of aortic valve disease.

Diagnostic performance of IVUS-FFR analysis based on generative adversarial network and bifurcation fractal law for assessing myocardial ischemia

Background

IVUS-based virtual FFR (IVUS-FFR) can provide additional functional assessment information to IVUS imaging for the diagnosis of coronary stenosis. IVUS image segmentation and side branch blood flow can affect the accuracy of virtual FFR. The purpose of this study was to evaluate the diagnostic performance of an IVUS-FFR analysis based on generative adversarial networks and bifurcation fractal law, using invasive FFR as a reference.

Method

In this study, a total of 108 vessels were retrospectively collected from 87 patients who underwent IVUS and invasive FFR. IVUS-FFR was performed by analysts who were blinded to invasive FFR. We evaluated the diagnostic performance and computation time of IVUS-FFR, and compared it with that of the FFR-branch (considering side branch blood flow by manually extending the side branch from the bifurcation ostia). We also compared the effects of three bifurcation fractal laws on the accuracy of IVUS-FFR.

Result

The diagnostic accuracy, sensitivity, and specificity for IVUS-FFR to identify invasive FFR≤0.80 were 90.7% (95% CI, 83.6–95.5), 89.7% (95% CI, 78.8–96.1), 92.0% (95% CI, 80.8–97.8), respectively. A good correlation and agreement between IVUS-FFR and invasive FFR were observed. And the average computation time of IVUS-FFR was shorter than that of FFR-branch. In addition to this, we also observe that the HK model is the most accurate among the three bifurcation fractal laws.

Conclusion

Our proposed IVUS-FFR analysis correlates and agrees well with invasive FFR and shows good diagnostic performance. Compared with FFR-branch, IVUS-FFR has the same level of diagnostic performance with significantly lower computation time.

Flow patterns in the venous sinus of pulsatile tinnitus patients with transverse sinus stenosis and underlying vortical flow as a causative factor

BACKGROUND

Transverse sinus stenosis (TSS) is commonly found in Pulsatile Tinnitus (PT) patients. Vortex flow is prominent in venous sinus with stenosis, and so it is important to determine the distribution and strength of the vortical flow to understand its influence on the occurrence of PT.

METHODS

In this study, by using computational fluid dynamics for hemodynamic analysis in patient-specific geometries based on Magnetic Resonance Imaging (MRI), we have investigated the blood flow within the venous sinus of 16 subjects with PT. We have employed both laminar and turbulent flow models for simulations, to obtain (i) streamlines of velocity distribution in the venous sinus, and (ii) pressure distributions of flow patterns in the venous sinus. Then, hemodynamic analysis in the venous sinus recirculation zone was carried out, to determine the flow patterns at the junction of transverse sinuses and sigmoid sinuses. Finally, we have proposed a new model for turbulence evaluation based on the regression analysis of anatomic and hemodynamics parameters.

RESULTS

Correlation analysis between the anatomical parameters and the hemodynamic parameters has shown that stenosis at the transverse sinus was the main factor in the local hemodynamics variation in the venous sinus of patients; in this context, it is shown that vorticity can be used as a prime indicator of the severity of the stenosis function. Our results have shown a significant correlation between the vorticity and the stenotic maximum velocity (SMV) (r = 0.282, p = 0.004). Then, a parameterized prediction model is proposed to determine the vorticity in terms of flow and anatomic variables, termed as the turbulence eddy prediction model (TEP model). Our result have shown that the TEP model is sensitive to the dominant flow distribution, with a high correlation to the flow-based vorticity (r = 0.809, p = 0.009).

CONCLUSIONS

The quantification of the vorticity (as both vorticity and MVV) in the downstream of TSS could be a marker for indication of turbulent energy at the transverse-sigmoid sinus, which could potentially serve as a hemodynamic marker for the functional assessment of the PT-related TSS.